Statistics in SPSS - JSM406

• Anglický název: Statistics in SPSS
• Zajišťuje: Katedra sociologie (23-KS)
• Fakulta: Fakulta sociálních věd
• Platnost: od 2022 do 2022
• Semestr: oba
• E-Kredity: 8
• Rozsah, examinace: 1/1, Zk [HT]
• Počet míst: zimní:neurčen / 50 (20); letní:neurčen / neurčen (20)
• Minimální obsazenost: neomezen
• Virtuální mobilita / počet míst pro virtuální mobilitu: ne
• Stav předmětu: vyučován
• Jazyk výuky: angličtina
• Způsob výuky: prezenční
• Způsob výuky: prezenční
• Poznámka: předmět je možno zapsat mimo plán; povolen pro zápis po webu; při zápisu přednost, je-li ve stud. plánu; předmět lze zapsat v ZS i LS
• Garant: PhDr. Ing. Petr Soukup, Ph.D.; Mgr. Ivan Petrúšek
• Vyučující: Mgr. Ivan Petrúšek; PhDr. Ing. Petr Soukup, Ph.D.; Mgr. Tereza Svobodová
• Třída: Courses for incoming students
• Je prerekvizitou pro: JSM503
• Je záměnnost pro: JSM513

Anotace

Poslední úprava: Mgr. Ivan Petrúšek (18.02.2023)

The course will introduce students to the basic statistical methods used in quantitative social science research. As this is an introductory course, no previous knowledge of statistics is required. Students will learn and practice basic statistical methods by analyzing sociological survey data in the IBM SPSS program (Statistical Product and Service Solutions). After taking this course, students should be able to prepare a data set, perform data management tasks and analyze data using basic statistical techniques. Students of Sociology of Contemporary Societies, Society, Communication and Media, and other programmes accredited at the Institute of Sociology will get priority enrolment in the course.

This course is not intended for students who graduated from bachelor´s degree programmes at the Institute of Sociological Studies, Faculty of Social Sciences (SOSP, SOCSS, and SOCSA).

Literatura

Poslední úprava: Mgr. Ivan Petrúšek (31.01.2023)

Mandatory:

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Fourth edition. London: Sage.

(detailed reading assignment from the course textbook will be specified after each class)

Recommended:

deVaus, D. (2002). Surveys in social research. London: Routledge - Taylor & Francis Group.

Recommended book in the Czech language: Mareš, P. - Rabušic, L. - Soukup, P. (2015): Analýza sociálněvědních dat (nejen) v SPSS. Brno: muniPRESS. (ch. 2 - ch. 10)

Metody výuky

Poslední úprava: Mgr. Ivan Petrúšek (31.01.2023)

The classes are a combination of lectures and seminars. The first part of each class (approx. 40 minutes) is a lecture during which the tutor introduces key concepts in statistical theory and data analysis methods (see syllabus below). The second part (approx. 40 minutes) is a seminar where students apply the methods introduced during the lecture in the IBM SPSS environment. Institute of Sociological Studies will provide the enrolled students with the IBM SPSS licence (so that they will have the software installed on their personal computers).

This course is taught in person during the summer semester of 2022-2023 (room number 308).

The classes will also be streamed via zoom on the following link: https://cuni-cz.zoom.us/j/98994074855

 

Požadavky ke zkoušce

Poslední úprava: Mgr. Ivan Petrúšek (31.01.2023)

Grading is based on homework assignments (7 assignments, each worth 5 points) and a final exam (worth 65 points). Students may earn up to 100 total points.

The deadline for homework assignments is Thursday (11:59 pm), and assignments are submitted via email ([e-mail]). Students have nine days to prepare and submit each homework assignment.

Grading:

• 91 - 100 points = grade A
• 81 - 90 points = grade B
• 71 - 80 points = grade C
• 61 - 70 points = grade D
• 51 - 60 points = grade E
• 0 - 50 points = not passed (grade F)

NOTE: Total points earned are rounded to the whole number (e. g. the overall result of 50.5 points is rounded to 51 points and corresponds to the grade E).

Sylabus

Poslední úprava: Mgr. Ivan Petrúšek (31.01.2023)

Course Schedule

Week 1: Course overview. Introduction to SPSS environment.
Week 2: Descriptive vs inferential statistics. Levels of measurement.
Week 3: Introduction to probability and probability distributions.
Week 4: Sampling variation. Central limit theorem. Confidence intervals (for the mean).
Week 5: Statistical hypotheses testing framework. One-sample t-test.
Week 6: Independent-samples t-test. Paired-samples t-test.
Week 7: Exploring assumptions of parametric tests. Assumption of normality.
Week 8: Analysis of variance (within- and between-group variability, F-test, post-hoc tests).
Week 9: Correlation analysis (Covariance, Pearson and Spearman correlation coefficients, Scatterplot).
Week 10: Linear regression (method of least squares, simple/multiple regression).
Week 11: Analysis of categorical data I (confidence interval for a proportion, introduction to crosstabs).
Week 12: Analysis of categorical data II (chi-square test of independence, contingency coefficients, residuals).
Week 13: Review session.